Python Institute PCED-30-02認証資格、PCED-30-02試験合格攻略

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Python Institute PCED-30-02 Exam Syllabus Topics:

SectionWeightObjectives
Introduction to Data and Data Analysis Concepts22.5%- Data Analysis Process and Workflow
  • 1. Describe the steps of the data analysis process
  • 2. Identify common data sources and collection methods
  • 3. Explain the role of data cleaning and preparation
- Define and Classify Data
  • 1. Differentiate structured, semi-structured, and unstructured data
  • 2. Explain how data becomes meaningful
  • 3. Classify data as quantitative or qualitative
- Data Ethics and Privacy
  • 1. Recognize ethical considerations in data handling
  • 2. Understand basic data privacy concepts
Working with Data and Performing Simple Analyses32.5%- Data Cleaning and Transformation
  • 1. Handle missing and inconsistent data
  • 2. Perform basic data transformation (filtering, sorting, grouping)
- Data Analysis with Python Libraries
  • 1. Utilize the collections module for specialized containers
  • 2. Use the math and statistics modules for basic calculations
  • 3. Perform basic operations with NumPy arrays
  • 4. Work with the datetime module for date/time data
- Simple Analytical Techniques
  • 1. Identify basic patterns and trends in data
  • 2. Calculate descriptive statistics (mean, median, mode)
Communicating Insights and Reporting12.5%- Data Storytelling and Reporting
  • 1. Create clear and concise analytical reports
  • 2. Present insights with visual and verbal techniques
  • 3. Structure insights as a narrative
- Data Visualization
  • 1. Select appropriate visuals for different data types
  • 2. Recognize common visualization types (bar, line, pie charts)
  • 3. Interpret simple data visualizations
Python Basics for Data Analysis32.5%- Python Fundamentals
  • 1. Use variables, data types, and basic operators
  • 2. Implement control flow structures (loops, conditionals)
  • 3. Define and use functions
- File Handling
  • 1. Read from and write to files (text, CSV)
  • 2. Use the csv module for basic CSV operations
- Data Structures
  • 1. Apply common operations and methods to data structures
  • 2. Work with lists, tuples, dictionaries, and sets

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Python Institute PCED - Certified Entry-Level Data Analyst with Python 認定 PCED-30-02 試験問題 (Q27-Q32):

質問 # 27
You are creating a presentation slide to communicate findings about student stress levels across four categories over a six-month period. You include the following line graph in your presentation:

Your goal is to help your audience clearly understand trends in stress levels over time.
What is the highest problem with this visual presentation? Select the best answer.

正解：E

解説：
The lines are very light and visually similar, making it difficult to clearly distinguish between the different stress level trends, which directly reduces the effectiveness of the visualization in communicating patterns over time.


質問 # 28
A list is defined as [1, 2, 3]. The programmer uses the method .append([4, 5]). What will the resulting list look like after execution?

正解：D

解説：
The append() method adds its argument as a single element. Therefore, the list [4, 5] is added as one nested list, resulting in [1, 2, 3, [4, 5]].


質問 # 29
Which of the following code snippets will output Truefor both printfunctions by correctly identifying the type of each variable and performing valid operations?

正解：D

解説：
Adding an integer and a float produces a float result, so checking that the type of the sum is float evaluates to True. The variable holding 3.0 is a float, so the isinstance check for float also evaluates to True.


質問 # 30
Which task is traditionally or typically performed by a data analyst, and not by a data scientist or analytics specialist? Select the best answer.

正解：A

解説：
Creating summary tables and charts to describe patterns in historical data is a core responsibility of a data analyst, focusing on descriptive analytics and reporting rather than building predictive models, machine learning systems, or engineering data pipelines.


質問 # 31
A government agency collects citizen data for various public services.
What is the primary reason for emphasizing careful data management throughout the entire data lifecycle in this context? Select the best answer.

正解：B

解説：
Careful data management across the entire lifecycle ensures high data quality for reliable public services, protects sensitive citizen information from misuse, and maintains compliance with legal and regulatory data protection requirements.


質問 # 32
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